International journal for parasitology: drugs and drug

AI631510
, 
AI631511
, 
AI632198
, and 
BF223228
), a B-cell
tumor from a chronic lymphatic leukemia (GenBank accession no. 
BE675048
), and a T-cell lymphoma
(GenBank accession no. 
AA361117
)′ .
Additionally, any multiple alignments of nucleotide or protein data must be submitted to a
recognised database and must also receive a unique accession number. The accession number can
appear in the text in the relevant section of the Results, as: ′ Alignment files are available by
anonymous FTP from FTP.EBI.AC.UK in directory/pub/databases/embl/align or via the EMBLALIGN
database via SRS at 
http://srs.ebi.ac.uk
; under accession(s)′ . The usual method for submitting
alignments is by the World Wide Web to the European Bioinformatics Institute (via Webin-
Align: 
http://www.ebi.ac.uk
). Microarray data, in MIAME-compliant format, should be submitted
to ArrayExpress (
http://www.ebi.ac.uk/arrayexpress/
) or GEO (
http://www.ncbi.nlm.nih.gov/geo/
).
Accession identifiers relating to the data should be provided in the manuscript text.
Policy on bioinformatics papers.
In silico analysis: The following guidelines apply to papers
that exclusively use in silico analysis or rely heavily on this approach for analysis and conclusions.
Such papers should address a significant biological issue or issues. Bioinformatic data should be
supported by novel or published biological data. Work would typically use information from a number
of databases and even from a number of parasite or host species and use a number of analytical
methods. Types of ′ metaanalysis′ are encouraged either across a wide range of parasites or, say,
at a number of points in a metabolic or signalling pathway or an immune cascade. In silico analysis
may be especially suitable for review articles.
Guidelines for reporting of protein identifications using mass spectrometry
The following information should be provided for protein or peptide identifications using mass
spectrometry:
1. The program, and version number, used to create peak lists and the parameters used in the creation
of the list.
2. The program, and version number, of the program used for database searching. Parameters used for
searching should be specified, including, but not limited to, precursor-ion mass tolerance, fragment-
ion mass tolerance, modifications allowed for, missed cleavages and enzymes used in protein cleavage.
3. The name and version number of the sequence database used in searches. If a custom-made
database is used then complete information on the origin of the sequences and database size should
be disclosed. Given the dependence of scoring on database size, the use of a small database, or one
excluding contaminants, should be justified. 4. A short description of the methods use to interpret
the significance of search results, including any statistical analysis, confidence thresholds and other
values specific to judging the certainty of the identification.
5. For large-scale experiments a false-positive determination should be reported. This may be the
result of randomized database searches or other approaches.
6. Each protein identification should include the accession number, score generated by the search
algorithm used, sequence coverage and the number of unique peptide sequences assigned in the
protein identification.
7. Single peptide identifications should include an annotated MS/MS spectrum showing fragment
assignments together with the peptide sequence, precursor mass, charge and error.
8. Identifications arising from peptide mass fingerprinting should include an annotated mass
spectrum. The number of matched peaks, the number of unmatched peaks and the sequence coverage
should also be reported along with all parameters and thresholds used to analyse the data. This
includes mass accuracy, resolution, calibration methods, contaminant exclusions along with the
scoring scheme used and measure of the false-positive rate.
Author inquiries
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